Hybrid Particle and Kalman Filtering for Pupil Tracking in Active IR Illumination Gaze Tracking System

A novel pupil tracking method is proposed by combining particle filtering and Kalman filtering for the fast and accurate detection of pupil target in an active infrared source gaze tracking system. Firstly, we utilize particle filtering to track pupil in synthesis triple-channel color map (STCCM) for the fast detection and develop a comprehensive pupil motion model to conduct and analyze the randomness of pupil motion. Moreover, we built a pupil observational model based on the similarity measurement with generated histogram to improve the credibility of particle weights. Particle filtering can detect pupil region in adjacent frames rapidly. Secondly, we adopted Kalman filtering to estimate the pupil parameters more precisely. The state transitional equation of the Kalman filtering is determined by the particle filtering estimation, and the observation of the Kalman filtering is dependent on the detected pupil parameters in the corresponding region of difference images estimated by particle filtering. Tracking results of Kalman filtering are the final pupil target parameters. Experimental results demonstrated the effectiveness and feasibility of this method.Published versio

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Particle Filters for Colour-Based Face Tracking Under Varying Illumination

Automatic human face tracking is the basis of robotic and active vision systems used for facial feature analysis, automatic surveillance, video conferencing, intelligent transportation, human-computer interaction and many other applications. Superior human face tracking will allow future safety surveillance systems which monitor drowsy drivers, or patients and elderly people at the risk of seizure or sudden falls and will perform with lower risk of failure in unexpected situations. This area has actively been researched in the current literature in an attempt to make automatic face trackers more stable in challenging real-world environments. To detect faces in video sequences, features like colour, texture, intensity, shape or motion is used. Among these feature colour has been the most popular, because of its insensitivity to orientation and size changes and fast process-ability. The challenge of colour-based face trackers, however, has been dealing with the instability of trackers in case of colour changes due to the drastic variation in environmental illumination. Probabilistic tracking and the employment of particle filters as powerful Bayesian stochastic estimators, on the other hand, is increasing in the visual tracking field thanks to their ability to handle multi-modal distributions in cluttered scenes. Traditional particle filters utilize transition prior as importance sampling function, but this can result in poor posterior sampling. The objective of this research is to investigate and propose stable face tracker capable of dealing with challenges like rapid and random motion of head, scale changes when people are moving closer or further from the camera, motion of multiple people with close skin tones in the vicinity of the model person, presence of clutter and occlusion of face. The main focus has been on investigating an efficient method to address the sensitivity of the colour-based trackers in case of gradual or drastic illumination variations. The particle filter is used to overcome the instability of face trackers due to nonlinear and random head motions. To increase the traditional particle filter\u27s sampling efficiency an improved version of the particle filter is introduced that considers the latest measurements. This improved particle filter employs a new colour-based bottom-up approach that leads particles to generate an effective proposal distribution. The colour-based bottom-up approach is a classification technique for fast skin colour segmentation. This method is independent to distribution shape and does not require excessive memory storage or exhaustive prior training. Finally, to address the adaptability of the colour-based face tracker to illumination changes, an original likelihood model is proposed based of spatial rank information that considers both the illumination invariant colour ordering of a face\u27s pixels in an image or video frame and the spatial interaction between them. The original contribution of this work lies in the unique mixture of existing and proposed components to improve colour-base recognition and tracking of faces in complex scenes, especially where drastic illumination changes occur. Experimental results of the final version of the proposed face tracker, which combines the methods developed, are provided in the last chapter of this manuscript

A Multicamera System for Gesture Tracking With Three Dimensional Hand Pose Estimation

The goal of any visual tracking system is to successfully detect then follow an object of interest through a sequence of images. The difficulty of tracking an object depends on the dynamics, the motion and the characteristics of the object as well as on the environ ment. For example, tracking an articulated, self-occluding object such as a signing hand has proven to be a very difficult problem. The focus of this work is on tracking and pose estimation with applications to hand gesture interpretation. An approach that attempts to integrate the simplicity of a region tracker with single hand 3D pose estimation methods is presented. Additionally, this work delves into the pose estimation problem. This is ac complished by both analyzing hand templates composed of their morphological skeleton, and addressing the skeleton\u27s inherent instability. Ligature points along the skeleton are flagged in order to determine their effect on skeletal instabilities. Tested on real data, the analysis finds the flagging of ligature points to proportionally increase the match strength of high similarity image-template pairs by about 6%. The effectiveness of this approach is further demonstrated in a real-time multicamera hand tracking system that tracks hand gestures through three-dimensional space as well as estimate the three-dimensional pose of the hand

Development of a robust active infrared-based eye tracking system

Eye tracking has a number of useful applications ranging from monitoring a vehicle driver for possible signs of fatigue, providing an interface to enable severely disabled people to communicate with others, to a number of medical applications. Most eye tracking applications require a non-intrusive way of tracking the eyes, making a camera-based approach a natural choice. However, although significant progress has been made in recent years, modern eye tracking systems still have not overcome a number of challenges including eye occlusions, variable ambient lighting conditions and inter-subject variability. This thesis describes the complete design and implementation of a real-time camera-based eye tracker, which was developed mainly for indoor applications. The developed eye tracker relies on the so-called bright/dark pupil effect for both the eye detection and eye tracking phases. The bright/dark pupil effect was realised by the development of specialised hardware and near-infrared illumination, which were interfaced with a machine vision camera. For the eye detection phase the performance of three different types of classifiers, namely neurals networks, SVMs and AdaBoost were directly compared with each other on a dataset consisting of 17 individual subjects from different ethnic backgrounds. For the actual tracking of the eyes, a Kalman filter was combined with the mean-shift tracking algorithm. A PC application with a graphical user interface (GUI) was also developed to integrate the various aspects of the eye tracking system, which allows the user to easily configure and use the system. Experimental results have shown the eye detection phase to be very robust, whereas the eye tracking phase was also able to accurately track the eyes from frame-to-frame in real-time, given a few constraints. AFRIKAANS : Oogvolging het ’n beduidende aantal toepassings wat wissel van die deteksie van bestuurderuitputting, die voorsiening van ’n rekenaarintervlak vir ernstige fisies gestremde mense, tot ’n groot aantal mediese toepassings. Die meeste toepassings van oogvolging vereis ’n nie-indringende manier om die oë te volg, wat ’n kamera-gebaseerde benadering ’n natuurlike keuse maak. Alhoewel daar alreeds aansienlike vordering gemaak is in die afgelope jare, het moderne oogvolgingstelsels egter nogsteeds verskeie uitdagings nie oorkom nie, insluitende oog okklusies, veranderlike beligtingsomstandighede en variansies tussen gebruikers. Die verhandeling beskryf die volledige ontwerp en implementering van ’n kamera-gebaseerde oogvolgingsstelsel wat in reële tyd werk. Die ontwikkeling van die oogvolgingsstelsel maak staat op die sogenaamde helder/donker pupil effek vir beide die oogdeteksie en oogvolging fases. Die helder/donker pupil effek was moontlik gemaak deur die ontwikkeling van gespesialiseerde hardeware en naby-infrarooi illuminasie. Vir die oogdeteksie fase was die akkuraatheid van drie verskillende tipes klassifiseerders getoets en direk vergelyk, insluitende neurale netwerke, SVMs en AdaBoost. Die datastel waarmee die klassifiseerders getoets was, het bestaan uit 17 individuele toetskandidate van verskillende etniese groepe. Vir die oogvolgings fase was ’n Kalman filter gekombineer met die gemiddelde-verskuiwings algoritme. ’n Rekenaar program met ’n grafiese gebruikersintervlak was ontwikkel vir ’n persoonlike rekenaar, sodat al die verskillende aspekte van die oogvolgingsstelsel met gemak opgestel kon word. Eksperimentele resultate het getoon dat die oogdeteksie fase uiters akkuraat en robuust was, terwyl die oogvolgings fase ook hoogs akuraat die oë gevolg het, binne sekere beperkinge. CopyrightDissertation (MEng)--University of Pretoria, 2011.Electrical, Electronic and Computer Engineeringunrestricte

Robust and real-time hand detection and tracking in monocular video

In recent years, personal computing devices such as laptops, tablets and smartphones have become ubiquitous. Moreover, intelligent sensors are being integrated into many consumer devices such as eyeglasses, wristwatches and smart televisions. With the advent of touchscreen technology, a new human-computer interaction (HCI) paradigm arose that allows users to interface with their device in an intuitive manner. Using simple gestures, such as swipe or pinch movements, a touchscreen can be used to directly interact with a virtual environment. Nevertheless, touchscreens still form a physical barrier between the virtual interface and the real world. An increasingly popular field of research that tries to overcome this limitation, is video based gesture recognition, hand detection and hand tracking. Gesture based interaction allows the user to directly interact with the computer in a natural manner by exploring a virtual reality using nothing but his own body language. In this dissertation, we investigate how robust hand detection and tracking can be accomplished under real-time constraints. In the context of human-computer interaction, real-time is defined as both low latency and low complexity, such that a complete video frame can be processed before the next one becomes available. Furthermore, for practical applications, the algorithms should be robust to illumination changes, camera motion, and cluttered backgrounds in the scene. Finally, the system should be able to initialize automatically, and to detect and recover from tracking failure. We study a wide variety of existing algorithms, and propose significant improvements and novel methods to build a complete detection and tracking system that meets these requirements. Hand detection, hand tracking and hand segmentation are related yet technically different challenges. Whereas detection deals with finding an object in a static image, tracking considers temporal information and is used to track the position of an object over time, throughout a video sequence. Hand segmentation is the task of estimating the hand contour, thereby separating the object from its background. Detection of hands in individual video frames allows us to automatically initialize our tracking algorithm, and to detect and recover from tracking failure. Human hands are highly articulated objects, consisting of finger parts that are connected with joints. As a result, the appearance of a hand can vary greatly, depending on the assumed hand pose. Traditional detection algorithms often assume that the appearance of the object of interest can be described using a rigid model and therefore can not be used to robustly detect human hands. Therefore, we developed an algorithm that detects hands by exploiting their articulated nature. Instead of resorting to a template based approach, we probabilistically model the spatial relations between different hand parts, and the centroid of the hand. Detecting hand parts, such as fingertips, is much easier than detecting a complete hand. Based on our model of the spatial configuration of hand parts, the detected parts can be used to obtain an estimate of the complete hand's position. To comply with the real-time constraints, we developed techniques to speed-up the process by efficiently discarding unimportant information in the image. Experimental results show that our method is competitive with the state-of-the-art in object detection while providing a reduction in computational complexity with a factor 1 000. Furthermore, we showed that our algorithm can also be used to detect other articulated objects such as persons or animals and is therefore not restricted to the task of hand detection. Once a hand has been detected, a tracking algorithm can be used to continuously track its position in time. We developed a probabilistic tracking method that can cope with uncertainty caused by image noise, incorrect detections, changing illumination, and camera motion. Furthermore, our tracking system automatically determines the number of hands in the scene, and can cope with hands entering or leaving the video canvas. We introduced several novel techniques that greatly increase tracking robustness, and that can also be applied in other domains than hand tracking. To achieve real-time processing, we investigated several techniques to reduce the search space of the problem, and deliberately employ methods that are easily parallelized on modern hardware. Experimental results indicate that our methods outperform the state-of-the-art in hand tracking, while providing a much lower computational complexity. One of the methods used by our probabilistic tracking algorithm, is optical flow estimation. Optical flow is defined as a 2D vector field describing the apparent velocities of objects in a 3D scene, projected onto the image plane. Optical flow is known to be used by many insects and birds to visually track objects and to estimate their ego-motion. However, most optical flow estimation methods described in literature are either too slow to be used in real-time applications, or are not robust to illumination changes and fast motion. We therefore developed an optical flow algorithm that can cope with large displacements, and that is illumination independent. Furthermore, we introduce a regularization technique that ensures a smooth flow-field. This regularization scheme effectively reduces the number of noisy and incorrect flow-vector estimates, while maintaining the ability to handle motion discontinuities caused by object boundaries in the scene. The above methods are combined into a hand tracking framework which can be used for interactive applications in unconstrained environments. To demonstrate the possibilities of gesture based human-computer interaction, we developed a new type of computer display. This display is completely transparent, allowing multiple users to perform collaborative tasks while maintaining eye contact. Furthermore, our display produces an image that seems to float in thin air, such that users can touch the virtual image with their hands. This floating imaging display has been showcased on several national and international events and tradeshows. The research that is described in this dissertation has been evaluated thoroughly by comparing detection and tracking results with those obtained by state-of-the-art algorithms. These comparisons show that the proposed methods outperform most algorithms in terms of accuracy, while achieving a much lower computational complexity, resulting in a real-time implementation. Results are discussed in depth at the end of each chapter. This research further resulted in an international journal publication; a second journal paper that has been submitted and is under review at the time of writing this dissertation; nine international conference publications; a national conference publication; a commercial license agreement concerning the research results; two hardware prototypes of a new type of computer display; and a software demonstrator

A Comparative Evaluation of the Detection and Tracking Capability Between Novel Event-Based and Conventional Frame-Based Sensors

Traditional frame-based technology continues to suffer from motion blur, low dynamic range, speed limitations and high data storage requirements. Event-based sensors offer a potential solution to these challenges. This research centers around a comparative assessment of frame and event-based object detection and tracking. A basic frame-based algorithm is used to compare against two different event-based algorithms. First event-based pseudo-frames were parsed through standard frame-based algorithms and secondly, target tracks were constructed directly from filtered events. The findings show there is significant value in pursuing the technology further

Multi-sensor fusion for human-robot interaction in crowded environments

For challenges associated with the ageing population, robot assistants are becoming a promising solution. Human-Robot Interaction (HRI) allows a robot to understand the intention of humans in an environment and react accordingly. This thesis proposes HRI techniques to facilitate the transition of robots from lab-based research to real-world environments. The HRI aspects addressed in this thesis are illustrated in the following scenario: an elderly person, engaged in conversation with friends, wishes to attract a robot's attention. This composite task consists of many problems. The robot must detect and track the subject in a crowded environment. To engage with the user, it must track their hand movement. Knowledge of the subject's gaze would ensure that the robot doesn't react to the wrong person. Understanding the subject's group participation would enable the robot to respect existing human-human interaction. Many existing solutions to these problems are too constrained for natural HRI in crowded environments. Some require initial calibration or static backgrounds. Others deal poorly with occlusions, illumination changes, or real-time operation requirements. This work proposes algorithms that fuse multiple sensors to remove these restrictions and increase the accuracy over the state-of-the-art. The main contributions of this thesis are: A hand and body detection method, with a probabilistic algorithm for their real-time association when multiple users and hands are detected in crowded environments; An RGB-D sensor-fusion hand tracker, which increases position and velocity accuracy by combining a depth-image based hand detector with Monte-Carlo updates using colour images; A sensor-fusion gaze estimation system, combining IR and depth cameras on a mobile robot to give better accuracy than traditional visual methods, without the constraints of traditional IR techniques; A group detection method, based on sociological concepts of static and dynamic interactions, which incorporates real-time gaze estimates to enhance detection accuracy.Open Acces